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--- aira:start [2026/10/19 12:59] – [Schedule Autumn 2027] mzk
+++ aira:start [2026/10/19 13:00] (current) – [2026-10-15] mzk
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+==== 2026-10-22 ====
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+**Speaker**: Justin Liu,  PhD Candidate @ University of Waikato
+**Title**: Isolation-Based Anomaly Detection for Evolving Data Streams
+**Abstract**:
+Anomalies are rare, high-impact events, and detecting them in continuous data streams remains a persistent robustness challenge for deployed AI systems. Streaming settings break the assumptions most detectors rely on: each instance is seen once under bounded memory, the underlying distribution drifts over time, and labels are unavailable or expensive. My research develops four isolation-based detectors designed for these conditions. In the unsupervised domain, one preserves a global view of the cumulative distribution via reservoir sampling and incremental subtree regrowing, while the other tracks the recent local distribution using a sliding window and selectively replaces the weakest ensemble members. Building on these, two semi-supervised extensions use a small labelling budget, with label information guiding scoring, ensemble maintenance, and active learning.
+**Biogram**:
+Jia (Justin) Liu is a PhD candidate at the University of Waikato, New Zealand, supervised by Prof. Albert Bifet. His research focuses on streaming machine learning and anomaly detection, with particular emphasis on isolation forest methods for evolving data streams. His work addresses core challenges in stream learning, including concept drift adaptation, semi-supervised learning under label scarcity, and evaluation methodologies for imbalanced streaming data. His recent contributions include the Adaptive Isolation Forest (Discovery Science 2025) and the Streaming Isolation Forest (PAKDD 2025), alongside ongoing work on semi-supervised extensions of isolation-based detectors. He has submitted his doctoral thesis and expects to complete his degree in 2026.
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 ==== 2026-10-15 ====